结直肠癌免疫组化图像分级诊断方法

doi:10.16180/j.cnki.issn1007-7820.2024.12.004

Abstract

Abstract:

Human tissue pathology examination is mainly used for the diagnosis and treatment of various tumors. Immunohistochemical technique has important clinical significance in the early screening of colorectal cancer. In order to accurately determine the expression level of the tumor suppressor gene p53 in colorectal cancer, this study proposes a grading diagnostic method based on transfer learning with block-wise fine-tuning strategy. The parameters of the cell nucleus segmentation model are transferred to the diagnostic framework through image preprocessing, supervised model pre-training, and block-wise fine-tuning. The generated cell nucleus segmentation mask is subjected to PCA(Principal Component Analysis) dimensionality reduction and SVM(Support Vector Machine) multivariate classification to obtain the final image diagnosis result. The proposed method is verified on colorectal cancer p53 protein IHC(Immunohistochemistry) image dataset. Dice value of the model reaches 0.887 6 and classification accuracy reaches 90.28%. The results show that the proposed method can effectively grade the immunohistochemical images of colorectal cancer, and provide reliable auxiliary information for doctors to read the film.

Key words: immunohistochemistry, colorectal cancer, pathological diagnosis, supervised learning, transfer learning, cell segmentation, fine-tuning strategy, clustering

CLC Number:

TP391.41

MO Zhuorui, HUANG Qianghao, ZHANG Lin, CAO Yuqi, GE Weiting, YU Minghui. Grading and Diagnostic Method for Colorectal Cancer Immunohistochemical Images[J].Electronic Science and Technology, 2024, 37(12): 24-31.

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References 23

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最新释放网络块	对应输出维度	微调后模型Dice值
Block₈	128×128	0.752 3
Block₇	64×64	0.822 4
Block₆	32×32	0.783 6
Block₅	16×16	0.692 8

添加卷积层数	对应卷积层参数	Dice系数
1	卷积层(1,(1,1), 激活函数='Softmax')	0.822 4
2	卷积层(32,(3,3), 步长=(1,1), 激活函数='ReLU') 卷积层(1,(1,1), 激活函数='Softmax')	0.862 8
3	卷积层(64,(3,3), 步长=(1,1), 激活函数='ReLU') 卷积层(32,(3,3), 步长=(1,1), 激活函数='ReLU') 卷积层(1,(1,1), 激活函数='Softmax')	0.831 2

微调操作	平均Dice系数
迁移预训练网络权重重构最后一层为卷积(1,(1,1))	0.624 9
释放Block₇~Block₈网络块并微调模型	0.822 4
在倒数第二层的位置添加卷积(32,(3,3))	0.862 8
结合结直肠癌IHC分级诊断标签筛选训练数据	0.887 6

序号	主成分个数	测试集占比	迭代误差精度	图像域	准确率 /%
1	150	0.2	1×10^-9	BGR	72.30
2	200	0.2	1×10^-9	BGR	84.50
3	220	0.2	1×10^-9	BGR	80.20
4	200	0.2	1×10^-6	BGR	76.60
5	200	0.2	1×10^-9	BGR	84.50
6	200	0.2	1×10^-12	BGR	86.10
7	200	0.2	1×10^-12	YUV	78.90
8	200	0.2	1×10^-12	HSV	90.28

Grading and Diagnostic Method for Colorectal Cancer Immunohistochemical Images

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